RESOURCE ALLOCATION AND OPTIMIZATION OF RECONFIGURABLE INTELLIGENT SURFACES IN WIRELESS NETWORKS

Abstract

In a wireless network Reconfigurable Intelligent Surface has recently gained popularity among researchers. The Intelligent Surface differs from all devices by its reflective passive elements that can adjust the phase shift of the signal at the present time. Since nowadays more and more devices are being added to the wireless network, it becomes more difficult to control the connection between transmitters and receivers. Therefore, the purpose of this research is to reproduce the operation of an Intelligent Surface for servicing several devices simultaneously by Rayleigh and Nakagami-m distributions. With a large number of devices, everyone knows that the spectral efficiency and overall performance of model system decrease with increasing number of users. Therefore, this study presents optimization using a Genetic Algorithm. Thus, it becomes possible to reduce interference between two points and increase the spectral efficiency of communication process. In connection with the results found, it can be summarized that the system model using a Genetic Algorithm demonstrates the highest results. To confirm this assumption, numerical calculations and simulations of the system are given by using a Reconfigurable Intelligent Surface. What is more an experiment was conducted by adding the Power Control, which makes it possible for the Reconfigurable Intelligent Surface to work continuously. Since Power Control gives a guarantee of providing energy at any time. This can provide a reduction in power consumption and lead to greater opportunities in the field of wireless communication. Furthermore, performance analysis demonstrates that numerous factors might have an impact on system efficiency, such as number of RIS reflective elements, transmit power, task size and others. Numerical and simulation results can provide a detailed understanding of the behavior of a system under different conditions and can help improve analytical system models.